Abstract:

Some user needs can only be met by leveraging the capabilities of others to undertake particular tasks that require intelligence
and labor. Crowdsourcing such capabilities is one way to achieve this. But providing a service that leverages crowd
intelligence and labor is a challenge, since various factors need to be considered to enable reliable service provisioning. For
example, the selection of an optimal set of workers from those who bid to perform a task needs to be made based on their
reliability, expected reward, and distance to the target locations. Moreover, for an application involving multiple services,
the overall cost and time constraints must be optimally allocated to each involved service. In this paper, we develop a framework,
named CROWDSERVICE, which supplies crowd intelligence and labor as publicly accessible crowd services via mobile
crowdsourcing. The paper extends our earlier work by providing an approach for constraints synthesis and worker selection.
It employs a genetic algorithm to dynamically synthesize and update near-optimal cost and time constraints for each crowd
service involved in a composite service, and selects a near-optimal set of workers for each crowd service to be executed. We
implement the proposed framework on Android platforms, and evaluate its effectiveness, scalability and usability in both
experimental and user studies.

Some user needs in real life can only be accomplished by lever-aging the intelligence and labor of other people via crowdsourcing tasks. For example, one may want to confirm the validity of the description of a secondhand ...

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